Compatibilité OpenAI

Les modèles Gemini sont accessibles à l'aide des bibliothèques OpenAI (Python et TypeScript/JavaScript), ainsi que de l'API REST. Seule l'authentification Google Cloud est compatible avec la bibliothèque OpenAI dans Vertex AI. Si vous n'utilisez pas encore les bibliothèques OpenAI, nous vous recommandons d'appeler directement l'API Gemini.

Python

import openai
from google.auth import default
import google.auth.transport.requests

# TODO(developer): Update and un-comment below lines
#project_id = "PROJECT_ID"
location = "us-central1"

# # Programmatically get an access token
credentials, _ = default(scopes=["https://www.googleapis.com/auth/cloud-platform"])
credentials.refresh(google.auth.transport.requests.Request())

# OpenAI Client
client = openai.OpenAI(
  base_url=f"https://{location}-aiplatform.googleapis.com/v1/projects/{project_id}/locations/{location}/endpoints/openapi",
  api_key=credentials.token
)

response = client.chat.completions.create(
  model="google/gemini-2.0-flash-001",
  messages=[
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "Explain to me how AI works"}
  ]
)

print(response.choices[0].message)

Qu'est-ce qui a changé ?

  • api_key=credentials.token : pour utiliser l'authentification Google Cloud , obtenez un jeton d'authentificationGoogle Cloud à l'aide de l'exemple de code.

  • base_url : indique à la bibliothèque OpenAI d'envoyer les requêtes à Google Cloudau lieu de l'URL par défaut.

  • model="google/gemini-2.0-flash-001" : choisissez un modèle Gemini compatible parmi ceux hébergés par Vertex.

Raisonnement

Les modèles Gemini 2.5 sont entraînés à réfléchir à des problèmes complexes, ce qui améliore considérablement leur capacité de raisonnement. L'API Gemini est fournie avec un paramètre de "budget de réflexion" qui permet de contrôler précisément le temps de réflexion du modèle.

Contrairement à l'API Gemini, l'API OpenAI propose trois niveaux de contrôle de la réflexion : "faible", "moyen" et "élevé", qui sont mappés en arrière-plan sur des budgets de jetons de réflexion de 1 000, 8 000 et 24 000.

Pour désactiver la réflexion, définissez l'effort de raisonnement sur None.

Python

import openai
from google.auth import default
import google.auth.transport.requests

# TODO(developer): Update and un-comment below lines
#project_id = PROJECT_ID
location = "us-central1"

# # Programmatically get an access token
credentials, _ = default(scopes=["https://www.googleapis.com/auth/cloud-platform"])
credentials.refresh(google.auth.transport.requests.Request())

# OpenAI Client
client = openai.OpenAI(
  base_url=f"https://{location}-aiplatform.googleapis.com/v1/projects/{project_id}/locations/{location}/endpoints/openapi",
  api_key=credentials.token
)

response = client.chat.completions.create(
  model="google/gemini-2.5-flash-preview-04-17",
  reasoning_effort="low",
  messages=[
      {"role": "system", "content": "You are a helpful assistant."},
      {
          "role": "user",
          "content": "Explain to me how AI works"
      }
  ]
)
print(response.choices[0].message)

Streaming

L'API Gemini est compatible avec les réponses en flux continu.

Python

import openai
from google.auth import default
import google.auth.transport.requests

# TODO(developer): Update and un-comment below lines
#project_id = PROJECT_ID
location = "us-central1"

credentials, _ = default(scopes=["https://www.googleapis.com/auth/cloud-platform"])
credentials.refresh(google.auth.transport.requests.Request())

client = openai.OpenAI(
  base_url=f"https://{location}-aiplatform.googleapis.com/v1/projects/{project_id}/locations/{location}/endpoints/openapi",
  api_key=credentials.token
)
response = client.chat.completions.create(
model="google/gemini-2.0-flash",
messages=[
  {"role": "system", "content": "You are a helpful assistant."},
  {"role": "user", "content": "Hello!"}
],
stream=True
)

for chunk in response:
  print(chunk.choices[0].delta)

Appel de fonction

L'appel de fonction facilite l'obtention de sorties de données structurées à partir de modèles génératifs. Il est compatible avec l'API Gemini.

Python

import openai
from google.auth import default
import google.auth.transport.requests

# TODO(developer): Update and un-comment below lines
#project_id = PROJECT_ID
location = "us-central1"

credentials, _ = default(scopes=["https://www.googleapis.com/auth/cloud-platform"])
credentials.refresh(google.auth.transport.requests.Request())

client = openai.OpenAI(
  base_url=f"https://{location}-aiplatform.googleapis.com/v1/projects/{project_id}/locations/{location}/endpoints/openapi",
  api_key=credentials.token
)

tools = [
{
  "type": "function",
  "function": {
    "name": "get_weather",
    "description": "Get the weather in a given location",
    "parameters": {
      "type": "object",
      "properties": {
        "location": {
          "type": "string",
          "description": "The city and state, e.g. Chicago, IL",
        },
        "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
      },
      "required": ["location"],
    },
  }
}
]

messages = [{"role": "user", "content": "What's the weather like in Chicago today?"}]
response = client.chat.completions.create(
model="google/gemini-2.0-flash",
messages=messages,
tools=tools,
tool_choice="auto"
)

print(response)

Compréhension des images

Les modèles Gemini sont multimodaux par nature et offrent les meilleures performances de leur catégorie pour de nombreuses tâches de vision courantes.

Python

from google.auth import default
import google.auth.transport.requests

import base64
from openai import OpenAI

# TODO(developer): Update and un-comment below lines
# project_id = "PROJECT_ID"
location = "us-central1"

# Programmatically get an access token
credentials, _ = default(scopes=["https://www.googleapis.com/auth/cloud-platform"])
credentials.refresh(google.auth.transport.requests.Request())

# OpenAI Client
client = openai.OpenAI(
  base_url=f"https://{location}-aiplatform.googleapis.com/v1/projects/{project_id}/locations/{location}/endpoints/openapi",
  api_key=credentials.token,
)

# Function to encode the image
def encode_image(image_path):
with open(image_path, "rb") as image_file:
  return base64.b64encode(image_file.read()).decode('utf-8')

# Getting the base64 string
#base64_image = encode_image("Path/to/image.jpeg")

response = client.chat.completions.create(
model="google/gemini-2.0-flash",
messages=[
  {
    "role": "user",
    "content": [
      {
        "type": "text",
        "text": "What is in this image?",
      },
      {
        "type": "image_url",
        "image_url": {
          "url":  f"data:image/jpeg;base64,{base64_image}"
        },
      },
    ],
  }
],
)

print(response.choices[0])

Générer une image

Python

from google.auth import default
import google.auth.transport.requests

import base64
from openai import OpenAI

# TODO(developer): Update and un-comment below lines
# project_id = "PROJECT_ID"
location = "us-central1"

# Programmatically get an access token
credentials, _ = default(scopes=["https://www.googleapis.com/auth/cloud-platform"])
credentials.refresh(google.auth.transport.requests.Request())

# OpenAI Client
client = openai.OpenAI(
  base_url=f"https://{location}-aiplatform.googleapis.com/v1/projects/{project_id}/locations/{location}/endpoints/openapi",
  api_key=credentials.token,
)

# Function to encode the image
def encode_image(image_path):
with open(image_path, "rb") as image_file:
  return base64.b64encode(image_file.read()).decode('utf-8')

# Getting the base64 string
#base64_image = encode_image("Path/to/image.jpeg")
base64_image = encode_image("/content/wayfairsofa.jpg")

response = client.chat.completions.create(
model="google/gemini-2.0-flash",
messages=[
  {
    "role": "user",
    "content": [
      {
        "type": "text",
        "text": "What is in this image?",
      },
      {
        "type": "image_url",
        "image_url": {
          "url":  f"data:image/jpeg;base64,{base64_image}"
        },
      },
    ],
  }
],
)

print(response.choices[0])

Compréhension audio

Analyser l'entrée audio :

Python

from google.auth import default
import google.auth.transport.requests

import base64
from openai import OpenAI

# TODO(developer): Update and un-comment below lines
# project_id = "PROJECT_ID"
location = "us-central1"

# Programmatically get an access token
credentials, _ = default(scopes=["https://www.googleapis.com/auth/cloud-platform"])
credentials.refresh(google.auth.transport.requests.Request())

# OpenAI Client
client = openai.OpenAI(
  base_url=f"https://{location}-aiplatform.googleapis.com/v1/projects/{project_id}/locations/{location}/endpoints/openapi",
  api_key=credentials.token,
)

with open("/path/to/your/audio/file.wav", "rb") as audio_file:
base64_audio = base64.b64encode(audio_file.read()).decode('utf-8')

response = client.chat.completions.create(
  model="gemini-2.0-flash",
  messages=[
  {
    "role": "user",
    "content": [
      {
        "type": "text",
        "text": "Transcribe this audio",
      },
      {
            "type": "input_audio",
            "input_audio": {
              "data": base64_audio,
              "format": "wav"
        }
      }
    ],
  }
],
)

print(response.choices[0].message.content)

Sortie structurée

Les modèles Gemini peuvent générer des objets JSON dans n'importe quelle structure que vous définissez.

Python

from google.auth import default
import google.auth.transport.requests

from pydantic import BaseModel
from openai import OpenAI

# TODO(developer): Update and un-comment below lines
# project_id = "PROJECT_ID"
location = "us-central1"

# Programmatically get an access token
credentials, _ = default(scopes=["https://www.googleapis.com/auth/cloud-platform"])
credentials.refresh(google.auth.transport.requests.Request())

# OpenAI Client
client = openai.OpenAI(
  base_url=f"https://{location}-aiplatform.googleapis.com/v1/projects/{project_id}/locations/{location}/endpoints/openapi",
  api_key=credentials.token,
)

class CalendarEvent(BaseModel):
  name: str
  date: str
  participants: list[str]

completion = client.beta.chat.completions.parse(
  model="google/gemini-2.0-flash",
  messages=[
      {"role": "system", "content": "Extract the event information."},
      {"role": "user", "content": "John and Susan are going to an AI conference on Friday."},
  ],
  response_format=CalendarEvent,
)

print(completion.choices[0].message.parsed)

Limites actuelles

  • Par défaut, les jetons d'accès sont valables une heure. Une fois qu'ils ont expiré, ils doivent être actualisés. Pour en savoir plus, consultez cet exemple de code.

  • La compatibilité avec les bibliothèques OpenAI est toujours en version bêta, car nous étendons la compatibilité des fonctionnalités. Pour toute question ou problème, publiez un message dans la communauté Google Cloud .

Étapes suivantes